Over the past decades, various methods have been proposed to estimate daily milk yields from partial yields. Many of these methods divide milking interval time into varied classes, assuming that the yield correction factors are constant within classes but vary between classes. The DeLorenzo and Wiggans (D-W) method has been widely used in the United States, typically following a 2-step process. It calculates discrete yield factors for segmented milking interval classes and then refines them through a follow-up smoothing step. Such a 2-step approach is computationally inefficient, and discrete yield correction factors introduce biases. This study explored strategies to integrate continuous yield factors into established methods, exemplified by the D-W method. The renovated method, also called the polynomial-interaction regression model, postulates multiplicative yield correction factors as a linear or quadratic function of milking interval time, operating on interactions with partial yields. It uses all available data in a single step, exhibiting greater computability efficiency and higher estimation accuracy. A reparameterization leads to a linear model, making estimating the model parameters convenient. We evaluated the performance of the revised methods using a previous dataset of milking records from Holstein cows compared with some existing methods. The results showed that the refurbished model gave more accurate estimates of daily milk yields.

Johne's disease (JD) is an infectious enteric disease in ruminants, causing substantial economic loss annually worldwide. This work aimed to estimate JD's genetic parameters and the phenotypic and genetic trends by incorporating recent data. It also explores the feasibility of a national genetic evaluation for JD susceptibility in Holstein cattle in the United States. The data were extracted from a JD data repository, maintained at the Council on Dairy Cattle Breeding, and initially supplied by 2 dairy record processing centers. The data comprised 365,980 Holstein cows from 1,048 herds participating in a voluntary control program for JD. Two protocol kits, IDEXX Paratuberculosis Screening Ab Test (IDX) and Parachek 2 (PCK), were used to analyze milk samples with the ELISA technique. Test results from the first 5 parities were considered. An animal was considered infected if it had at least one positive outcome. The overall average of JD incidence was 4.72% in these US Holstein cattle. Genotypes of 78,964 SNP markers were used for 25,000 animals randomly selected from the phenotyped populaestimated based on 3 models, namely, a pedigree-only (ssTHR), and a single-step linear model (ssLR). The posto moderate: 0.11 to 0.16 based on the 2 threshold models from 0.18 (THR) to 0.22 (ssLR) for IDX and from 0.14 economically important traits such as milk yield, milk protein, milk fat, somatic cell score, and mastitis were low, indicating a nonoverlapping genetic selection process with traits in current genetic evaluations. Our results suggest the feasibility of reducing the JD incidence rate by incorporating it into the national genetic evaluation programs.

In the United States, lactation milk yields are not measured directly but are calculated from the test-day milk yields. Still, test-day milk yields are estimated from partial yields obtained from single milkings. Various methods have been proposed to estimate test-day milk yields, primarily to deal with unequal milking intervals dating back to the 1970s and 1980s. The Wiggans model is a de facto method for estimating test-day milk yields in the United States, which was initially proposed for cows milked 3 times daily, assuming a linear relationship between a proportional test-day milk yield and milking interval. However, the linearity assumption did not hold precisely in Holstein cows milked twice daily because of prolonged and uneven milking intervals. The present study reviewed and evaluated the nonlinear models that extended the Wiggans model for estimating daily or test-day milk yields. These nonlinear models, except step functions, demonstrated smaller errors and greater accuracies for estimated test-day milk yields compared with the conventional methods. The nonlinear models offered additional benefits. For example, the locally weighted regression model (e.g., locally estimated scatterplot smoothing) could utilize data information in scalable neighborhoods and weigh observations according to their distance in milking interval time. General additive models provide a flexible, unified framework to model nonlinear predictor variables additively. Another drawback of the conventional methods is a loss of accuracy caused by discretizing milking interval time into large bins while deriving multiplicative correction factors for estimating test-day milk yields. To overcome this problem, we proposed a general approach that allows milk yield correction factors to be derived for every possible milking interval time, resulting in more accurately estimated test-day milk yields. This approach can be applied to any model, including nonparametric models.

In Table 1, the 2003 values for lifetime net merit dollars were not provided for service-sire calving ease or daughter calving ease. The values are shown in bold below.Table 1Traits included in USDA selection indices1PD$ = predicted difference dollars (Dickinson et al., 1971); MFP$ = milk-fat-protein dollars (Norman et al., 2010); NM$ = lifetime net merit dollars (VanRaden and Wiggans, 1995). and the relative emphasis placed on each, from 1971 to 2021 (adapted from VanRaden et al., 2021)Trait2PL = productive life; BWC = body weight composite; UDC = udder composite; FLC = feet and legs composite; DPR = daughter pregnancy rate; SCE = sire (direct) calving ease; DCE = daughter (maternal) calving ease; CA$ = calving ability dollars; HCR = heifer conception rate; CCR = cow conception rate; LIV = cow livability; HTH$ = health dollars; RFI = residual feed intake; EFC = early first calving; HLIV = heifer livability.Relative emphasis on trait (%)PD$ (1971)MFP$ (1976)NM$ (1994)NM$ (2000)NM$ (2003)NM$ (2006)NM$ (2010)NM$ (2014)NM$ (2017)NM$ (2018)NM$ (2021)Milk522765000−1−1−10Fat4846252122231922242722Protein—27433633231620181717PL——201411172219131215SCS——−6−9−9−9−10−7−7−4−3BWC———−4−3−4−6−5−6−5−9UDC———77678773FLC———44343331DPR————79117775SCE————−2——————DCE————−2——————CA$—————655553HCR———————1111CCR———————2221LIV————————774HTH$—————————22RFI——————————−12EFC——————————1HLIV——————————11 PD$ = predicted difference dollars (Dickinson et al., 1971); MFP$ = milk-fat-protein dollars (Norman et al., 2010); NM$ = lifetime net merit dollars (VanRaden and Wiggans, 1995).2 PL = productive life; BWC = body weight composite; UDC = udder composite; FLC = feet and legs composite; DPR = daughter pregnancy rate; SCE = sire (direct) calving ease; DCE = daughter (maternal) calving ease; CA$ = calving ability dollars; HCR = heifer conception rate; CCR = cow conception rate; LIV = cow livability; HTH$ = health dollars; RFI = residual feed intake; EFC = early first calving; HLIV = heifer livability. Open table in a new tab The authors regret the error. Changes in genetic trends in US dairy cattle since the implementation of genomic selectionJournal of Dairy ScienceVol. 106Issue 2PreviewGenomic selection increases accuracy and decreases generation interval, accelerating genetic changes in populations. Assumptions of genetic improvement must be addressed to quantify the magnitude and direction of change. Genetic trends of US dairy cattle breeds were examined to determine the genetic gain since the implementation of genomic evaluations in 2009. Inbreeding levels and generation intervals were also investigated. Breeds included Ayrshire, Brown Swiss, Guernsey, Holstein (HO), and Jersey (JE), which were characterized by the evaluation breed the animal received. Full-Text PDF Open Access

Genomic selection increases accuracy and decreases generation interval, accelerating genetic changes in populations. Assumptions of genetic improvement must be addressed to quantify the magnitude and direction of change. Genetic trends of US dairy cattle breeds were examined to determine the genetic gain since the implementation of genomic evaluations in 2009. Inbreeding levels and generation intervals were also investigated. Breeds included Ayrshire, Brown Swiss, Guernsey, Holstein (HO), and Jersey (JE), which were characterized by the evaluation breed the animal received. Mean genomic predicted breeding values (PBV¯) were analyzed per year to calculate genetic trends for bulls and cows. The data set contained 154,008 bulls and 33,022,242 cows born since 1975. Breakpoints were estimated using linear regression, and nonlinear regression was used to fit the piecewise model for the small sample number in some years. Generation intervals and inbreeding levels were also investigated since 1975. Milk, fat, and protein yields, somatic cell score, productive life, daughter pregnancy rate, and livability PBV¯ were documented. In 2017, 100% of bulls in this data set were genotyped. The percentage of genotyped cows has increased 23 percentage points since 2010. Overall, production traits have increased steadily over time, as expected. The HO and JE breeds have benefited most from genomics, with up to 192% increase in genetic gain since 2009. Due to the low number of observations, trends for Ayrshire, Brown Swiss, and Guernsey are difficult to infer from. Trends in fertility are most substantial; particularly, most breeds are trending downwards and daughter pregnancy rate for JE has been decreasing steadily since 1975 for bulls and cows. Levels of genomic inbreeding are increasing in HO bulls and cows. In 2017, genomic inbreeding levels were 12.7% for bulls and 7.9% for cows. A suggestion to control this is to include the genomic inbreeding coefficient with a negative weight to the selection index of bulls with high future genomic inbreeding levels. For sires of bulls, the current generation intervals are 2.2 yr in HO, 3.2 in JE, 4.4 in Brown Swiss, 5.1 in Ayrshire, and 4.3 in Guernsey. The number of colored breed bulls in the United States is currently at an extremely low level, and this number will only increase with a market incentive or additional breed association involvement. Increased education and extension could be beneficial to increase knowledge about inbreeding levels, use of genomics and genetic improvement, and genetic diversity in the genomic selection era.

Various methods have been proposed to estimate daily yield from partial yields, primarily to deal with unequal milking intervals. This paper offers an exhaustive review of daily milk yields, the foundation of lactation records. Seminal advancements in the late 20th century concentrated on two main adjustment metrics: additive additive correction factors (ACF) and multiplicative correction factors (MCF). An ACF model provides additive adjustments to two times AM or PM milk yield, which then becomes the estimated daily yields, whereas an MCF is a ratio of daily yield to the yield from a single milking. Recent studies highlight the potential of alternative approaches, such as exponential regression and other nonlinear models. Biologically, milk secretion rates are not linear throughout the entire milking interval, influenced by the internal mammary gland pressure. Consequently, nonlinear models are appealing for estimating daily milk yields as well. MCFs and ACFs are typically determined for discrete milking interval classes. Nonetheless, large discrete intervals can introduce systematic biases. A universal solution for deriving continuous correction factors has been proposed, ensuring reduced bias and enhanced daily milk yield estimation accuracy. When leveraging test-day milk yields for genetic evaluations in dairy cattle, two predominant statistical models are employed: lactation and test-day yield models. A lactation model capitalizes on the high heritability of total lactation yields, aligning closely with dairy producers' needs because the total amount of milk production in a lactation directly determines farm revenue. However, a lactation yield model without harnessing all test-day records may ignore vital data about the shapes of lactation curves needed for informed breeding decisions. In contrast, a test-day model emphasizes individual test-day data, accommodating various intervals and recording plans and allowing the estimation of environmental effects on specific test days. In the United States, the patenting of test-day models in 1993 used to restrict the use of test-day models to regional and unofficial evaluations by the patent holders. Estimated test-day milk yields have been used as if they were accurate depictions of actual milk yields, neglecting possible estimation errors. Its potential consequences on subsequent genetic evaluations have not been sufficiently addressed. Moving forward, there are still numerous questions and challenges in this domain.

This study compared 3 correlational (best prediction, linear regression, and feed-forward neural networks) and 2 causal models (recursive structural equation model and recurrent neural networks) for estimating lactation milk yields. The correlational models assumed associations between test-day milk yields (health conditions), while the casual models postulated unidirectional recursive effects between these test-day variables. Wood lactation curves were used to simulate the data and served as a benchmark model. Individual Wood lactation curves provided an excellent parametric interpretation of lactation dynamics, with their prediction accuracies depending on the coverage of the lactation curve dynamics. Best prediction outperformed other models in the absence of mastitis but was suboptimal when mastitis was present and unaccounted for. Recurrent neural networks yielded the highest accuracy when mastitis was present. Although causal models facilitated the inference about the causality underlying lactation, precisely capturing the causal relationships was challenging because the underlying biology was complex. Misspecification of recursive effects in the recursive structural equation model resulted in a loss of accuracy. Hence, modeling causal relationships does not necessarily guarantee improved accuracies. In practice, a parsimonious model is preferred, balancing model complexity and accuracy. In addition to the choice of statistical models, the proper accounting for factors and covariates affecting milk yields is equally crucial.

Cows are typically milked 2 or more times on a test-day, but not all these milkings are sampled and weighed. The initial approach estimated a test-day yield with doubled morning (AM) or evening (PM) yield in the AM-PM milking plans, assuming equal AM and PM milking intervals. However, AM and PM milking intervals can vary, and milk secretion rates may be different between day and night. Statistical methods have been proposed to estimate daily yields in dairy cows, focusing on various yield correction factors in 2 broad categories: additive correction factors (ACF) and multiplicative correction factors (MCF). The ACF are evaluated by the average differences between AM and PM milk yield for various milking interval classes, coupled with other categorical variables. We show that an ACF model is equivalent to a regression model of daily yield on categorical regressor variables, and a continuous variable for AM or PM yield with a fixed regression coefficient of 2.0. Similarly, a linear regression model can be implemented as an ACF model with the regression coefficient for AM or PM yield estimated from the data. The linear regression models improved the accuracy of the estimates compared with the ACF models. The MCF are ratios of daily yield to yield from single milkings, but their statistical interpretations vary. Overall, MCF were more accurate for estimating daily milk yield than ACF. The MCF have biological and statistical challenges. Systematic biases occurred when ACF or MCF were computed on discretized milking interval classes, leading to accuracy loss. An exponential regression model was proposed as an alternative model for estimating daily milk yields, which improved the accuracy. Characterization of ACF and MCF showed how they improved the accuracy compared with doubling AM or PM yield as the daily milk yield. All the methods performed similarly with equal AM and PM milkings. The methods were explicitly described to estimate daily milk yield in AM and PM milking plans. Still, the principles generally apply to cows milked more than 2 times a day and apply similarly to the estimation of daily fat and protein yields with some necessary modifications.

Residual feed intake (RFI) and feed saved (FS) are important feed efficiency traits that have been increasingly considered in genetic improvement programs. Future sustainability of these genetic evaluations will depend upon greater flexibility to accommodate sparsely recorded dry matter intake (DMI) records on many more cows, especially from commercial environments. Recent multiple-trait random regression (MTRR) modeling developments have facilitated days in milk (DIM)-specific inferences on RFI and FS, particularly in modeling the effect of change in metabolic body weight (MBW). The MTRR analyses, using daily data on the core traits of DMI, MBW, and milk energy (MilkE), were conducted separately for 2,532 primiparous and 2,379 multiparous US Holstein cows from 50 to 200 DIM. Estimated MTRR variance components were used to derive genetic RFI and FS and DIM-specific genetic partial regressions of DMI on MBW, MilkE, and change in MBW. Estimated daily heritabilities of RFI and FS varied across lactation for both primiparous (0.05-0.07 and 0.11-0.17, respectively) and multiparous (0.03-0.13 and 0.10-0.17, respectively) cows. Genetic correlations of RFI across DIM varied (>0.05) widely compared with FS (>0.54) within either parity class. Heritability estimates based on average lactation-wise measures were substantially larger than daily heritabilities, ranging from 0.17 to 0.25 for RFI and from 0.35 to 0.41 for FS. The partial genetic regression coefficients of DMI on MBW (0.11 to 0.16 kg/kg0.75 for primiparous and 0.12 to 0.14 kg/kg0.75 for multiparous cows) and of DMI on MilkE (0.45 to 0.68 kg/Mcal for primiparous and 0.36 to 0.61 kg/Mcal for multiparous cows) also varied across lactation. In spite of the computational challenges encountered with MTRR, the model potentially facilitates an efficient strategy for harnessing more data involving a wide variety of data recording scenarios for genetic evaluations on feed efficiency.

Genetic selection has been an effective strategy to improve calving traits including stillbirth in dairy cattle. The primary objectives of the present study were to characterize stillbirth data and determine the feasibility of implementing routine genetic evaluations of stillbirth in five non-Holstein dairy breeds, namely Ayrshire, Guernsey, Milking Shorthorn, Brown Swiss, and Jersey. An updated sire-maternal grandsire threshold model was used to estimate genetic parameters and genetic values for stillbirth. Stillbirth data with the birth years of dams from 1995 to 2018 were extracted from the United States national calving ease database maintained by the Council on Dairy Cattle Breeding. The extracted stillbirth records varied drastically among the five dairy breeds. There were approximately 486K stillbirth records for Jersey and more than 80K stillbirth records for Brown Swiss. The direct and maternal heritability estimates of stillbirth were 6.0% (4.5–7.6%) and 4.7% (3.3–6.1%) in Jersey and 6.8% (3.2–10.5%) and 1.1% (0.6–2.9%) in Brown Swiss. The estimated genetic correlations between direct and maternal genetic effects for stillbirth were −0.15 (−0.38 to −0.08) in Jersey and −0.35 (−0.47 to −0.12) in Brown Swiss. The estimated genetic parameters for stillbirth in these two breeds were within close ranges of previous studies. The reliabilities of predicted transmitting abilities in Jersey and Brown Swiss increased substantially, thanks to the substantial increase in available stillbirth data in the past 10 years. The stillbirth records for Ayrshire, Guernsey, and Milking Shorthorn, which ranged approximately between 3K and 12K, are insufficient to implement reliable routine genetic evaluations of stillbirth in these three dairy breeds. Estimated genetic (co)variances and genetic values deviated considerably from the reported ranges of previous studies, and the reliabilities of predicted transmitting abilities were low in these three breeds. In conclusion, routine genetic evaluations of stillbirth are feasible in Brown Swiss and Jersey. However, reliable genetic evaluations of stillbirth in Ayrshire, Guernsey, and Milking Shorthorn require further data collection on stillbirth.

Multistep genomic evaluations for US dairy cattle were first implemented in 2009 for Holstein, Jersey, and Brown Swiss dairy cattle breeds. In contrast, the adoption of genomics for Ayrshire and Guernsey is more recent. We investigated the genetic trends, inbreeding levels, and generation intervals since 1975 for the five dairy cattle breeds receiving genomic evaluations. Overall, production traits have increased steadily over time, as expected. Holstein and Jersey have benefited most from genomics, with up to a 6-fold increase in genetic gain in fat yield for Holstein bulls since 2009, nonetheless, with a rise in inbreeding levels. The progress made by breeds other than Holstein and Jersey is still small because of the limited portfolio of bulls available and the lower number of genotyped and phenotyped animals. Expanding the benefits of genomics for those breeds will require more comprehensive adoption of this technology and market incentives.

Cost-effective milking plans have been adapted to supplement the standard supervised twice-daily monthly testing scheme since the 1960s. Various methods have been proposed to estimate daily milk yields (DMY), focusing on yield correction factors. The present study evaluated the performance of existing statistical methods, including a recently proposed exponential regression model, for estimating DMY using 10-fold cross-validation in Holstein and Jersey cows. The initial approach doubled the morning (AM) or evening (PM) yield as estimated DMY in AM-PM plans, assuming equal 12-h AM and PM milking intervals. However, in reality, AM milking intervals tended to be longer than PM milking intervals. Additive correction factors (ACF) provided additive adjustments beyond twice AM or PM yields. Hence, an ACF model equivalently assumed a fixed regression coefficient or a multiplier of “2.0” for AM or PM yields. Similarly, a linear regression model was viewed as an ACF model, yet it estimated the regression coefficient for a single milk yield from the data. Multiplicative correction factors (MCF) represented daily to partial milk yield ratios. Hence, multiplying a yield from single milking by an appropriate MCF gave a DMY estimate. The exponential regression model was analogous to an exponential growth function with the yield from single milking as the initial state and the rate of change tuned by a linear function of milking interval. In the present study, all the methods had high precision in the estimates, but they differed considerably in biases. Overall, the MCF and linear regression models had smaller squared biases and greater accuracies for estimating DMY than the ACF models. The exponential regression model had the greatest accuracies and smallest squared biases. Model parameters were compared. Discretized milking interval categories led to a loss of accuracy of the estimates. Characterization of ACF and MCF revealed their similarities and dissimilarities and biases aroused by unequal milking intervals. The present study focused on estimating DMY in AM-PM milking plans. Yet, the methods and relevant principles are generally applicable to cows milked more than two times a day.

Feed efficiency is a trait of significant economic and environmental importance in the dairy industry, and feed accounts for half of the costs of dairy production. Improvements in feed efficiency have the potential to reduce manure and methane outputs, as well as crop and land inputs. Measurements of feed efficiency rely on individual feed intake data however, these data are expensive and time-consuming to collect, resulting in an insufficient phenotyped population. A concerted effort has been underway in the United States for 10 years to collect data for genomic evaluations of feed efficiency. As a result of this effort, the Council on Dairy Cattle Breeding (CDCB; Bowie, MD) provided official evaluations for Feed Saved beginning in December 2020. Feed intake was measured for 4 to 6 wk in individual cows between 50 and 200 days-in-milk in 9 research herds; to date, we have amassed 655,000 daily records of intake and milk production. From these data, residual feed intake (RFI) is estimated with a linear model accounting for milk energy, metabolic body weight, change in body weight, and cohort effects. Current phenotypic data include 6,221 RFI records from 5,023 U.S. Holsteins born 1999 to 2017 (as of December 2020). Phenotypic RFI are used to estimate traditional PTA in a linear animal repeatability model. Deregressed traditional PTA are then used to calculate genomic evaluations of RFI. These evaluations are combined with evaluations for body weight composite (BWC) to provide Feed Saved evaluations to the dairy industry. Progeny-tested bulls have an average genomic reliability of 38% for Feed Saved. Comparatively, young bulls have an average genomic reliability of 28%. Given the expectedly low reliabilities, a primary goal continues to be collecting additional phenotypes. Emphasis is also directed towards ensuring that phenotyped cows have close ties to current bulls actively used by the dairy industry. International collaborations will further expand the reference population. As an example, the next official evaluation (April 2021) will include phenotypic data from Canada for 650 cow-lactations. Preliminary testing has indicated a 1 to 2% increase in genomic reliability from these additional data. Feed Saved is currently published by the CDCB as an individual trait. Future plans include incorporating the trait into an economic selection index.

There has been increasing interest in residual feed intake (RFI) as a measure of net feed efficiency in dairy cattle. Residual feed intake phenotypes are obtained as residuals from linear regression encompassing relevant factors (i.e., energy sinks) to account for body tissue mobilization. By rearranging the single-trait linear regression, we showed a causal RFI interpretation underlying the linear regression for RFI. It postulates recursive effects in energy allocation from energy sinks on dry matter intake, but the feedback or simultaneous effects are nonexistent. A Bayesian recursive structural equation model was proposed for directly predicting RFI and energy sinks and estimating relevant genetic parameters simultaneously. A simplified Markov chain Monte Carlo algorithm was described. The recursive model is asymptotically equivalent to one-step linear regression for RFI, yet extends the analytical capacity to multiple-trait analysis.

Since its first publication in Holsteins (HOL) in April 2018, and later inclusion of Jerseys (JER) in April 2020, US bull evaluations for mastitis resistance (MAS) have been exchanged with Interbull participating countries. Foreign phenotypes for MAS have been used since then to enhance the domestic reference population. Prior to April 2021, countries had the choice of exchanging pure clinical mastitis, somatic cell score, or a combination of methodologies where clinical/subclinical mastitis or a multi-trait approach using multiple sources of information. Hence, only IDs of bulls coming from certain countries with similar trait definitions and if the country of most daughters does not send only SCS were being used in the US. Effective in the April 2021 routine run, Interbull introduced a new trait named SNP training for clinical mastitis (STCM) to better estimate SNP effects specifically for clinical mastitis. The new edits in the US were validated in January 2021 in a full test run. In the April run, genotyped bulls with an international evaluation from the other participating countries used STCM results, whereas the previous trait (called hereafter as the MAS), that combines mastitis from some countries and correlated SCS from others continued to be used for non-genotyped animals. In order to evaluate the impact on the US evaluations, this study aimed to compare PTA and REL between December 2020 routine run (2012r) and January 2021 test run (2101t) and April routine run (2104r), for both HOL and JER breeds, but now taking into account the aforementioned criteria. Descriptive statistics, Pearson and Spearman correlations (rg) as well as regression coefficients (b1) by predicting MAS on STCM to measure potential biases, were calculated. The 2104r PTA means were slightly smaller for HOL and relatively smaller for JER in all scenarios. Pearson and Spearman correlations were always higher than 0.90 in all scenarios for both breeds, no matter the evaluation set of comparison. These results demonstrate a practically null impact on the US evaluations. By comparing 2012r and 2101t, the b1 values were, in general, close to 1 (range 0.98-1.06). On the other hand, a bit more bias can be seen by comparing 2012r with 2104r and 2101t with 2104r. These results may have been partially due to >30,000 corrected phenotypes received in the U.S. April evaluation. Our results suggest that minor impact is expected, and genetic progress improvement is enabled by the implementation of international STCM in the next routine run (April 2021). On the other hand, identifying which countries or individual bulls had direct MAS or only correlated SCS was previously difficult to automate correctly at the national level. Therefore, it is of most importance that Interbull continues to keep track of STCM countries, so several benefits such as gains in REL for bulls with many STCM daughters, would be achieved.

Genetic selection has been a very successful tool for the long-term improvement of livestock populations, and the rapid adoption of genomic selection over the last decade has doubled the rate of gain in some populations. Breeding programs seek to identify genetically superior parents of the next generation, typically as a function of an index that combines information about many economically important traits into a single number. In the United States, the data that drive this system are collected through the national dairy herd improvement program that began more than a century ago. The resulting information about animal performance, pedigree, and genotype is used to compute genomic evaluations for comparing and ranking animals for selection. However, the full expression of genetic potential requires that animals are placed in environments that can support such performance. The Agricultural Research Service of the US Department of Agriculture and the Council on Dairy Cattle Breeding collaborate to deliver state-of-the-art genomic evaluations to the dairy industry. Today, most breeding stock are selected and marketed using the net merit dollars (NM$) selection index, which evolved from 2 traits in 1926 (milk and fat yield) to a combination of 36 individual traits following the last NM$ update in 2018. Updates to NM$ require the estimation of many different values, and it can be difficult to achieve consensus from stakeholders on what should be added to, or removed from, the index at each review, and how those traits should be weighted. Over time, the majority of the emphasis in the index has shifted from yield traits to fertility, health, and fitness traits. Phenotypes for some of these new traits are difficult or expensive to measure, or require changes to on-farm habits that have not been widely adopted. This is driving interest in sensor-based systems that provide continuous measurements of the farm environment, individual animal performance, and detailed milk composition. There is also a need to capture more detailed data about the environment in which animals perform, including information about feeding, housing, milking systems, and infectious and parasitic load. However, many challenges accompany these new technologies, including a lack of standardization or validation, need for high-speed internet connections, increased computational requirements, and interpretations that are often not backed by direct observations of biological phenomena. This work will describe how US selection objectives are developed, as well as discuss opportunities and challenges associated with new technologies for measuring and recording animal performance.